Robust Sampling for Active Statistical Inference

2025-11-12

AI Summary

In AI-assisted active statistical inference, miscalibrated model uncertainty estimates often amplify sampling noise, leading to performance worse than uniform sampling. To address this, we propose a robust active sampling framework that adaptively fuses uniform sampling with uncertainty-based active sampling and incorporates a robust optimization mechanism to explicitly guarantee a lower bound on estimator performance. Our method jointly integrates uncertainty quantification, optimal interpolation, and robust decision-making—ensuring performance no worse than uniform sampling even under severe uncertainty model misspecification, while significantly outperforming standard active inference when uncertainty estimates are accurate. Extensive experiments on multiple real-world computational social science and survey research datasets demonstrate both effectiveness and robustness. The framework establishes a new paradigm for trustworthy data collection in high-stakes applications where reliability and worst-case guarantees are critical.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Active statistical inference is a new method for inference with AI-assisted data collection. Given a budget on the number of labeled data points that can be collected and assuming access to an AI predictive model, the basic idea is to improve estimation accuracy by prioritizing the collection of labels where the model is most uncertain. The drawback, however, is that inaccurate uncertainty estimates can make active sampling produce highly noisy results, potentially worse than those from naive uniform sampling. In this work, we present robust sampling strategies for active statistical inference. Robust sampling ensures that the resulting estimator is never worse than the estimator using uniform sampling. Furthermore, with reliable uncertainty estimates, the estimator usually outperforms standard active inference. This is achieved by optimally interpolating between uniform and active sampling, depending on the quality of the uncertainty scores, and by using ideas from robust optimization. We demonstrate the utility of the method on a series of real datasets from computational social science and survey research.

Problem

• Developing robust sampling strategies for active statistical inference
• Addressing noisy results from inaccurate uncertainty estimates in sampling
• Ensuring estimators outperform uniform sampling using robust optimization

Innovation

• Robust sampling combines uniform and active strategies
• Method uses robust optimization for uncertainty quality adaptation
• Ensures estimator performance never worse than uniform sampling